Year: 2025
Type: conference-paper
Abstract: Large language model (LLM)-based agents have shown promise in tackling complex tasks by interacting dynamically with the environment.Existing work primarily focuses on behavior cloning from expert demonstrations or preference learning through exploratory trajectory sampling.However, these methods often struggle to address long-horizon tasks, where suboptimal actions accumulate step by step, causing agents to deviate from correct task trajectories.To address this, we highlight the importance of timely calibration and the need to automatically construct calibration trajectories for training agents.We propose Step-Level Trajectory Calibration (STeCa), a novel framework for LLM agent learning.Specifically, STeCa identifies suboptimal actions through a step-level reward comparison during exploration.It constructs calibrated trajectories using LLM-driven reflection, enabling agents to learn from improved decisionmaking processes.We finally leverage these calibrated trajectories with successful trajectories for reinforced training.Extensive experiments demonstrate that STeCa significantly outperforms existing methods.Further analysis highlights that timely calibration enables agents to complete tasks with greater robustness.
Authors: Hanlin Wang, Jian Wang, Chak Tou Leong, Wenjie Li
Institution: Hong Kong Polytechnic University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412888257